“…[41]). A series of Large-Eddy Simulations (LES) of turbulent boundary-layer flows with wall-normal blowing control were performed by Kametani et al [22] with a focus on the effect of intermittent blowing along the direction of the flow. By considering only part of the input power required to generate the wall-blowing, namely the pressure difference across the blowing wall, a very optimistic idealised net-power saving of around 18% was predicted.…”
Section: Introductionmentioning
confidence: 99%
“…wall-normal standing waves) are contained in the set of possible blowing strategies. These parameter ranges were chosen to coincide with recent successful applications of low-amplitude wallnormal blowing control (Kametani et al[22], Stroh et al[41]). Selecting the Bayesian optimisation hyper-parameters, i.e.…”
A Bayesian optimisation framework is developed to optimise low-amplitude wall-normal blowing control of a turbulent boundary-layer flow. The Bayesian optimisation framework determines the optimum blowing amplitude and blowing coverage to achieve up to a 5% net-power saving solution within 20 optimisation iterations, requiring 20 Direct Numerical Simulations (DNS). The power input required to generate the low-amplitude wall-normal blowing is measured experimentally for two different types of blowing device, and is used in the simulations to assess control performance. Wall-normal blowing with amplitudes of less than 1% of the free-stream velocity generate a skinfriction drag reduction of up to 76% over the control region, with a drag reduction which persists for up to 650δ 0 downstream of actuation (where δ 0 is the boundary-layer thickness at the start of the simulation domain). It is shown that it is the slow spatial recovery of the turbulent boundary-layer flow downstream of control which generates the net-power savings in this study. The downstream recovery of the skin-friction drag force is decomposed using the Fukagata-Iwamoto-Kasagi (FIK) identity, which shows that the generation of the net-power savings is due to changes in contributions to both the convection and streamwise development terms of the turbulent boundary-layer flow.
“…[41]). A series of Large-Eddy Simulations (LES) of turbulent boundary-layer flows with wall-normal blowing control were performed by Kametani et al [22] with a focus on the effect of intermittent blowing along the direction of the flow. By considering only part of the input power required to generate the wall-blowing, namely the pressure difference across the blowing wall, a very optimistic idealised net-power saving of around 18% was predicted.…”
Section: Introductionmentioning
confidence: 99%
“…wall-normal standing waves) are contained in the set of possible blowing strategies. These parameter ranges were chosen to coincide with recent successful applications of low-amplitude wallnormal blowing control (Kametani et al[22], Stroh et al[41]). Selecting the Bayesian optimisation hyper-parameters, i.e.…”
A Bayesian optimisation framework is developed to optimise low-amplitude wall-normal blowing control of a turbulent boundary-layer flow. The Bayesian optimisation framework determines the optimum blowing amplitude and blowing coverage to achieve up to a 5% net-power saving solution within 20 optimisation iterations, requiring 20 Direct Numerical Simulations (DNS). The power input required to generate the low-amplitude wall-normal blowing is measured experimentally for two different types of blowing device, and is used in the simulations to assess control performance. Wall-normal blowing with amplitudes of less than 1% of the free-stream velocity generate a skinfriction drag reduction of up to 76% over the control region, with a drag reduction which persists for up to 650δ 0 downstream of actuation (where δ 0 is the boundary-layer thickness at the start of the simulation domain). It is shown that it is the slow spatial recovery of the turbulent boundary-layer flow downstream of control which generates the net-power savings in this study. The downstream recovery of the skin-friction drag force is decomposed using the Fukagata-Iwamoto-Kasagi (FIK) identity, which shows that the generation of the net-power savings is due to changes in contributions to both the convection and streamwise development terms of the turbulent boundary-layer flow.
“…They also elucidated the reduction mechanism of turbulent skin friction by an analysis using Fukagata-Iwamoto-Kasagi (FIK) identity (Fukagata et al, 2002). In addition, friction drag reduction effect of UB was also confirmed at higher Reynolds numbers (Kametani et al, 2015) and Mach numbers (Kametani et al, 2017), and with intermittent slots (Kametani et al, 2016) and on a rough wall (Mori et al, 2017).…”
Friction drag reduction effect of a passive blowing on a Clark-Y airfoil is investigated. Uniform blowing, conducted in a wall-normal direction on a relatively wide surface, is generally known as an active control method for reduction of turbulent skin friction drag. In the present study, uniform blowing is passively driven by the pressure difference on a wing surface between suction and blowing regions. The suction and the blowing regions are respectively set around the leading edge and the rear part of the upper surface of the Clark-Y airfoil in order to ensure a sufficient pressure difference for passive blowing. The Reynolds number based on the chord length is 0.65×10 6 and 1.55×10 6. The angle of attack is set to 0 • and 6 •. The mean streamwise velocity profiles on the blowing region and the downstream, measured by a traversed hot-wire anemometry, are observed to shift away from the wall by passive blowing. This behavior qualitatively suggests reduction of local skin friction on the wing surface. A quantitative assessment of the friction drag is performed using the law of the wall accounting for pressure gradients (Nickels, 2004), coupled with a modified Stevenson's law (Vigdorovich, 2016) to account for the weak blowing. From this assessment, the local friction drag reduction effect of passive blowing is estimated to reach 4% − 23%.
“…(2015) and Kametani et al. (2016), where blowing and suction were used as the control mechanism, and Bannier, Garnier & Sagaut (2015), who analysed flows with drag reduction by riblets. The influence of the large scale structures in the boundary layer was investigated with the aid of FIK decomposition by Deck et al.…”
We show that the Fukagata et al.'s (Phys. Fluids, vol. 14, no. 11, 2002, pp. 73–76) identity for free-stream boundary layers simplifies to the von Kármán momentum integral equation relating the skin-friction coefficient and the momentum thickness when the upper bound in the integrals used to obtain the identity is taken to be asymptotically large. If a finite upper bound is used, the terms of the identity depend spuriously on the bound itself. Differently from channel and pipe flows, the impact of the Reynolds stresses on the wall-shear stress cannot be quantified in the case of free-stream boundary layers because the Reynolds stresses disappear from the identity. The infinite number of alternative identities obtained by performing additional integrations on the streamwise momentum equation also all simplify to the von Kármán equation. Analogous identities are found for channel flows, where the relative influence of the physical terms on the wall-shear stress depends on the number of successive integrations, demonstrating that the laminar and turbulent contributions to the skin-friction coefficient are only distinguished in the original identity discovered by Fukagata et al. (Phys. Fluids, vol. 14, no. 11, 2002, pp. 73–76). In the limit of large number of integrations, these identities degenerate to the definition of skin-friction coefficient and a novel twofold-integration identity is found for channel and pipe flows. In addition, we decompose the skin-friction coefficient uniquely as the sum of the change of integral thicknesses with the streamwise direction, following the study of Renard & Deck (J. Fluid Mech., vol. 790, 2016, pp. 339–367). We utilize an energy thickness and an inertia thickness, which is composed of a thickness related to the mean-flow wall-normal convection and a thickness linked to the streamwise inhomogeneity of the mean streamwise velocity. The contributions of the different terms of the streamwise momentum equation to the friction drag are thus quantified by these integral thicknesses.
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